If you thought AI was already overhyped, this week will have given you a heart attack. Controversial posts about a chatbot being shut down by FB appeared in several news outlets and are completely FALSE. The said research experiment simply didn’t yield the results the researchers expected and was aborted early. Also check out the reddit thread on the origins of the story.

Facebook has acquired AI assistant startup Ozlo to help build “compelling experiences within Messenger that are powered by artificial intelligence and machine learning,” a Facebook spokesperson has confirmed.

Despite the arXiv’s popularity, many authors are provoked by requests from reviewers that they cite papers which are only published on the arXiv preprint with the goal of flag-planting. Do you really have to? TLDR; Yes, you should.

How to use networks, such as those used in machine translation, that have already learned how to contextualize words to give new neural networks an advantage in learning to understand other parts of natural language.

Build a real bat detector using Tensorflow. The inputs are sound snippets of one second created by a detector device, which are then classified as either containing the sound of a bat, or not containing the sound of a bat.

A general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards building upon the Deep Deterministic Policy Gradient (DDPG) algorithm to use demonstrations. The demonstrations replace the need for carefully engineered rewards, and reduce the exploration problem encountered by classical RL approaches in these domains.

See this video for results. An approach to synthesize photographic images conditioned on semantic layouts. Given a semantic label map, the approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image.

The authors show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models.

Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis the authors obtain state-of-the-art performance on 8 benchmark datasets within sentiment, emotion and sarcasm detection using a single pretrained model.